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How to Manage Data Chaos in Healthcare Industry

Healthcare is one of the vital sectors in which a country’s development and growth rate gets determined. There is no need to explain the quantity of data (both structured and unstructured) that this sector produces. With excessive flow of data, a chaotic condition is created throughout the world due to the prevalence of different types of data sourcing tools.

Each patient visits multiple doctors, clinics, and hospitals to get treated for different types of diseases. Also, this data is not constant and needs to be updated regularly. Like a child’s data, who is being treated for typhoid is of no use when he/she needs to be diagnosed with diabetes in later stages of life. However, the same data can be useful to determine how a body of the child can react, if something similar happens at later stages of his/her life.

Support of latest technologies

The chaos can’t be simplified without adapting to the latest technologies rapidly like:

Big data usage: Foremost, the quantity of data which needs to be analyzed makes it necessary to move towards the usage of big data. This is one of those sectors which will never stop getting new data, which needs to be taken care of. This is where big data can play its role in supporting such huge chunks of data.

IoT improvements: IoT data is very crucial for healthcare sectors. It has been proved in the past; as well, that IoT driven data can significantly produce better results, if analyzed properly at the right time. It is this right time to merge this data, wherever required, that helps to get significant performance improvements.

AI categorize data chaos: Artificial Intelligence or AI has also a role to play in reducing data chaos. If implemented properly, AI can significantly delete or categorize the data which is no longer useful for analysis. Human intervention is not possible for this purpose because of the demand for maintaining the efficiency of this process.

Not only data integration and data analysis processes need to be optimized, but the data collection also needs to go through a lot of checks and balances. Unstructured data needs to be refined properly into a structured one and structured data needs to be standardized in such a way that it can accommodate the upcoming dynamic data efficiently.

Overall adaptation of good practices in data management can lead to reduced data chaos in healthcare and hence optimize results.



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